{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
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       "      <td>-3.808436</td>\n",
       "      <td>-2.022083</td>\n",
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       "      <td>13138.221690</td>\n",
       "      <td>-20741.615306</td>\n",
       "      <td>-0.907527</td>\n",
       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "      <td>-0.302590</td>\n",
       "      <td>-4.272617</td>\n",
       "      <td>-0.612796</td>\n",
       "      <td>-10555.500066</td>\n",
       "      <td>1649.289367</td>\n",
       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
       "      <td>1.722115</td>\n",
       "      <td>-9149.620794</td>\n",
       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                    epoch  sat_id             x             y  \\\n",
       "0   0  2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1  2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2  2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3  2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4  2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df = pd.read_csv(\"jan_train.csv\")\n",
    "meta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(meta_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(503227, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
       "       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
       "       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
       "       247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
       "       273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
       "       286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n",
       "       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
       "       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
       "       325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n",
       "       338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n",
       "       351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n",
       "       364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n",
       "       377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n",
       "       390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,\n",
       "       403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,\n",
       "       416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
       "       429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,\n",
       "       442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,\n",
       "       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,\n",
       "       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,\n",
       "       481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,\n",
       "       494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n",
       "       507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n",
       "       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,\n",
       "       533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,\n",
       "       546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,\n",
       "       559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,\n",
       "       572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,\n",
       "       585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,\n",
       "       598, 599], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_df['sat_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 503227 entries, 0 to 503226\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   id      503227 non-null  int64  \n",
      " 1   epoch   503227 non-null  object \n",
      " 2   sat_id  503227 non-null  int64  \n",
      " 3   x       503227 non-null  float64\n",
      " 4   y       503227 non-null  float64\n",
      " 5   z       503227 non-null  float64\n",
      " 6   Vx      503227 non-null  float64\n",
      " 7   Vy      503227 non-null  float64\n",
      " 8   Vz      503227 non-null  float64\n",
      " 9   x_sim   503227 non-null  float64\n",
      " 10  y_sim   503227 non-null  float64\n",
      " 11  z_sim   503227 non-null  float64\n",
      " 12  Vx_sim  503227 non-null  float64\n",
      " 13  Vy_sim  503227 non-null  float64\n",
      " 14  Vz_sim  503227 non-null  float64\n",
      "dtypes: float64(12), int64(2), object(1)\n",
      "memory usage: 57.6+ MB\n"
     ]
    }
   ],
   "source": [
    "meta_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis for SAT-0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = (meta_df[\"sat_id\"]==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1          True\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "          ...  \n",
       "503222    False\n",
       "503223    False\n",
       "503224    False\n",
       "503225    False\n",
       "503226    False\n",
       "Name: sat_id, Length: 503227, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat0_df = meta_df.loc[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>Vx</th>\n",
       "      <th>Vy</th>\n",
       "      <th>Vz</th>\n",
       "      <th>x_sim</th>\n",
       "      <th>y_sim</th>\n",
       "      <th>z_sim</th>\n",
       "      <th>Vx_sim</th>\n",
       "      <th>Vy_sim</th>\n",
       "      <th>Vz_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-01 00:00:00.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-8855.823863</td>\n",
       "      <td>13117.780146</td>\n",
       "      <td>-20728.353233</td>\n",
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       "      <td>-3.808436</td>\n",
       "      <td>-2.022083</td>\n",
       "      <td>-8843.131454</td>\n",
       "      <td>13138.221690</td>\n",
       "      <td>-20741.615306</td>\n",
       "      <td>-0.907527</td>\n",
       "      <td>-3.804930</td>\n",
       "      <td>-2.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-01 00:46:43.000</td>\n",
       "      <td>0</td>\n",
       "      <td>-10567.672384</td>\n",
       "      <td>1619.746066</td>\n",
       "      <td>-24451.813271</td>\n",
       "      <td>-0.302590</td>\n",
       "      <td>-4.272617</td>\n",
       "      <td>-0.612796</td>\n",
       "      <td>-10555.500066</td>\n",
       "      <td>1649.289367</td>\n",
       "      <td>-24473.089556</td>\n",
       "      <td>-0.303704</td>\n",
       "      <td>-4.269816</td>\n",
       "      <td>-0.616468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2014-01-01 01:33:26.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-10578.684043</td>\n",
       "      <td>-10180.467460</td>\n",
       "      <td>-24238.280949</td>\n",
       "      <td>0.277435</td>\n",
       "      <td>-4.047522</td>\n",
       "      <td>0.723155</td>\n",
       "      <td>-10571.858472</td>\n",
       "      <td>-10145.939908</td>\n",
       "      <td>-24271.169776</td>\n",
       "      <td>0.274880</td>\n",
       "      <td>-4.046788</td>\n",
       "      <td>0.718768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2014-01-01 02:20:09.001</td>\n",
       "      <td>0</td>\n",
       "      <td>-9148.251857</td>\n",
       "      <td>-20651.437460</td>\n",
       "      <td>-20720.381279</td>\n",
       "      <td>0.715600</td>\n",
       "      <td>-3.373762</td>\n",
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       "      <td>-20618.200201</td>\n",
       "      <td>-20765.019094</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>-3.375202</td>\n",
       "      <td>1.718306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2014-01-01 03:06:52.002</td>\n",
       "      <td>0</td>\n",
       "      <td>-6719.092336</td>\n",
       "      <td>-28929.061629</td>\n",
       "      <td>-14938.907967</td>\n",
       "      <td>0.992507</td>\n",
       "      <td>-2.519732</td>\n",
       "      <td>2.344703</td>\n",
       "      <td>-6729.358857</td>\n",
       "      <td>-28902.271436</td>\n",
       "      <td>-14992.399986</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>-2.522618</td>\n",
       "      <td>2.342237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                    epoch  sat_id             x             y  \\\n",
       "0   0  2014-01-01 00:00:00.000       0  -8855.823863  13117.780146   \n",
       "1   1  2014-01-01 00:46:43.000       0 -10567.672384   1619.746066   \n",
       "2   2  2014-01-01 01:33:26.001       0 -10578.684043 -10180.467460   \n",
       "3   3  2014-01-01 02:20:09.001       0  -9148.251857 -20651.437460   \n",
       "4   4  2014-01-01 03:06:52.002       0  -6719.092336 -28929.061629   \n",
       "\n",
       "              z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "0 -20728.353233 -0.908303 -3.808436 -2.022083  -8843.131454  13138.221690   \n",
       "1 -24451.813271 -0.302590 -4.272617 -0.612796 -10555.500066   1649.289367   \n",
       "2 -24238.280949  0.277435 -4.047522  0.723155 -10571.858472 -10145.939908   \n",
       "3 -20720.381279  0.715600 -3.373762  1.722115  -9149.620794 -20618.200201   \n",
       "4 -14938.907967  0.992507 -2.519732  2.344703  -6729.358857 -28902.271436   \n",
       "\n",
       "          z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "0 -20741.615306 -0.907527 -3.804930 -2.024133  \n",
       "1 -24473.089556 -0.303704 -4.269816 -0.616468  \n",
       "2 -24271.169776  0.274880 -4.046788  0.718768  \n",
       "3 -20765.019094  0.712437 -3.375202  1.718306  \n",
       "4 -14992.399986  0.989382 -2.522618  2.342237  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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       "      <th>737</th>\n",
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       "      <th>738</th>\n",
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       "      <td>2014-01-24 21:50:11.367</td>\n",
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       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>0</td>\n",
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       "      <td>23717.669835</td>\n",
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       "      <td>1.711798</td>\n",
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       "      <th>740</th>\n",
       "      <td>740</td>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
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      "text/plain": [
       "      id                    epoch  sat_id             x             y  \\\n",
       "736  736  2014-01-24 20:16:45.366       0  17323.752275  -2929.599994   \n",
       "737  737  2014-01-24 21:03:28.366       0  16803.492692   4510.550397   \n",
       "738  738  2014-01-24 21:50:11.367       0  15589.464016  11758.599852   \n",
       "739  739  2014-01-24 22:36:54.367       0  13644.957424  18446.405418   \n",
       "740  740  2014-01-24 23:23:37.368       0  10951.688066  24106.659914   \n",
       "\n",
       "                z        Vx        Vy        Vz         x_sim         y_sim  \\\n",
       "736  40015.054282 -0.066983  2.651528 -0.240019  16677.210531   5234.016160   \n",
       "737  38575.720092 -0.306878  2.639582 -0.792502  15395.263519  12449.405030   \n",
       "738  35543.718040 -0.561601  2.510341 -1.375365  13380.486475  19062.449796   \n",
       "739  30846.351215 -0.827062  2.233959 -1.978216  10617.516680  24593.429195   \n",
       "740  24456.597220 -1.093516  1.769369 -2.577266   7135.816766  28430.515190   \n",
       "\n",
       "            z_sim    Vx_sim    Vy_sim    Vz_sim  \n",
       "736  38340.904456 -0.330504  2.634282 -0.848806  \n",
       "737  35146.024515 -0.586354  2.491844 -1.435062  \n",
       "738  30278.225301 -0.852275  2.198419 -2.039662  \n",
       "739  23717.669835 -1.117669  1.711798 -2.636755  \n",
       "740  15552.366754 -1.359856  0.981096 -3.172296  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "sat0_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "Empty DataFrame\n",
       "Columns: [id, epoch, sat_id, x, y, z, Vx, Vy, Vz, x_sim, y_sim, z_sim, Vx_sim, Vy_sim, Vz_sim]\n",
       "Index: []"
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     "execution_count": 12,
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    "sat0_df.loc[sat0_df[\"sat_id\"]==1]"
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   "cell_type": "code",
   "execution_count": 13,
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   id      741 non-null    int64  \n",
      " 1   epoch   741 non-null    object \n",
      " 2   sat_id  741 non-null    int64  \n",
      " 3   x       741 non-null    float64\n",
      " 4   y       741 non-null    float64\n",
      " 5   z       741 non-null    float64\n",
      " 6   Vx      741 non-null    float64\n",
      " 7   Vy      741 non-null    float64\n",
      " 8   Vz      741 non-null    float64\n",
      " 9   x_sim   741 non-null    float64\n",
      " 10  y_sim   741 non-null    float64\n",
      " 11  z_sim   741 non-null    float64\n",
      " 12  Vx_sim  741 non-null    float64\n",
      " 13  Vy_sim  741 non-null    float64\n",
      " 14  Vz_sim  741 non-null    float64\n",
      "dtypes: float64(12), int64(2), object(1)\n",
      "memory usage: 92.6+ KB\n"
     ]
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   "source": [
    "sat0_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat0_df_univ = sat0_df[[\"epoch\", \"x\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>2014-01-01 03:53:35.002</td>\n",
       "      <td>-3708.453525</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-01 04:40:18.003</td>\n",
       "      <td>-437.699227</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-01 05:27:01.003</td>\n",
       "      <td>2863.147037</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-01 06:13:44.004</td>\n",
       "      <td>6031.593902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-01 07:00:27.004</td>\n",
       "      <td>8950.655291</td>\n",
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      "text/plain": [
       "                     epoch             x\n",
       "0  2014-01-01 00:00:00.000  -8855.823863\n",
       "1  2014-01-01 00:46:43.000 -10567.672384\n",
       "2  2014-01-01 01:33:26.001 -10578.684043\n",
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       "6  2014-01-01 04:40:18.003   -437.699227\n",
       "7  2014-01-01 05:27:01.003   2863.147037\n",
       "8  2014-01-01 06:13:44.004   6031.593902\n",
       "9  2014-01-01 07:00:27.004   8950.655291"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   epoch   741 non-null    object \n",
      " 1   x       741 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 17.4+ KB\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ[\"epoch\"] = pd.to_datetime(sat0_df_univ[\"epoch\"])"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                    epoch             x\n",
       "0 2014-01-01 00:00:00.000  -8855.823863\n",
       "1 2014-01-01 00:46:43.000 -10567.672384\n",
       "2 2014-01-01 01:33:26.001 -10578.684043\n",
       "3 2014-01-01 02:20:09.001  -9148.251857\n",
       "4 2014-01-01 03:06:52.002  -6719.092336"
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     },
     "execution_count": 18,
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   "source": [
    "sat0_df_univ.head()"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>2014-01-24 20:16:45.366</td>\n",
       "      <td>17323.752275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>2014-01-24 21:03:28.366</td>\n",
       "      <td>16803.492692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>2014-01-24 21:50:11.367</td>\n",
       "      <td>15589.464016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2014-01-24 22:36:54.367</td>\n",
       "      <td>13644.957424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2014-01-24 23:23:37.368</td>\n",
       "      <td>10951.688066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch             x\n",
       "736 2014-01-24 20:16:45.366  17323.752275\n",
       "737 2014-01-24 21:03:28.366  16803.492692\n",
       "738 2014-01-24 21:50:11.367  15589.464016\n",
       "739 2014-01-24 22:36:54.367  13644.957424\n",
       "740 2014-01-24 23:23:37.368  10951.688066"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 741 entries, 0 to 740\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   epoch   741 non-null    datetime64[ns]\n",
      " 1   x       741 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 17.4 KB\n"
     ]
    }
   ],
   "source": [
    "sat0_df_univ.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('23 days 23:23:37.368000')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat0_df_univ['epoch'].max() - sat0_df_univ['epoch'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(16, 10), dpi=80)\n",
    "\n",
    "plt.plot(sat0_df_univ[\"epoch\"], sat0_df_univ[\"x\"])\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"x co-ordinates\")\n",
    "plt.title(\"Position(x) of Satellite\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      741.000000\n",
       "mean      4912.520136\n",
       "std       9851.147157\n",
       "min     -10716.106467\n",
       "25%      -5156.221079\n",
       "50%       7005.209383\n",
       "75%      15173.743957\n",
       "max      17325.111650\n",
       "Name: x, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pos = sat0_df_univ[\"x\"]\n",
    "x_pos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = x_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine stationarity of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dickey-Fuller Test\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "adf, pvalue, usedlag_, nobs_, critical_values_, icbest_ = adfuller(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002473777001334866\n"
     ]
    }
   ],
   "source": [
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a707f1a808>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(741,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting using ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pmdarima in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (1.8.4)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (1.25.9)\n",
      "Requirement already satisfied: Cython!=0.29.18,>=0.29 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (0.29.21)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (1.4.1)\n",
      "Requirement already satisfied: pandas>=0.19 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (1.0.5)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ensorflow (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ensorflow (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ensorflow (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ensorflow (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (0.13.1)\n",
      "Requirement already satisfied: numpy>=1.19.3 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (1.19.5)\n",
      "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (49.2.0.post20200714)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (0.16.0)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pmdarima) (0.23.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from pandas>=0.19->pmdarima) (2020.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from scikit-learn>=0.22->pmdarima) (2.1.0)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.2)\n",
      "Requirement already satisfied: six in c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages (from patsy>=0.5.2->statsmodels!=0.12.0,>=0.11->pmdarima) (1.15.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: Ignoring invalid distribution -umpy (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ensorflow (c:\\users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima.arima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Autoarima gives us best model suited for the data\n",
    "# p - number of autoregressive terms (AR)\n",
    "# q - Number of moving avergae terms (MA)\n",
    "# d - number of non-seasonal differences\n",
    "#p, d, q represent non-seasonal components\n",
    "#P, D, Q represent seasonal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,1,0)[12]             : AIC=inf, Time=1.23 sec\n",
      " ARIMA(0,1,0)(0,1,0)[12]             : AIC=14492.621, Time=0.06 sec\n",
      " ARIMA(1,1,0)(1,1,0)[12]             : AIC=inf, Time=nan sec\n",
      " ARIMA(0,1,1)(0,1,1)[12]             : AIC=inf, Time=3.84 sec\n",
      " ARIMA(0,1,0)(1,1,0)[12]             : AIC=inf, Time=0.32 sec\n",
      " ARIMA(0,1,0)(0,1,1)[12]             : AIC=inf, Time=1.59 sec\n",
      " ARIMA(0,1,0)(1,1,1)[12]             : AIC=9338.871, Time=2.62 sec\n",
      " ARIMA(0,1,0)(2,1,1)[12]             : AIC=9340.651, Time=4.06 sec\n",
      " ARIMA(0,1,0)(1,1,2)[12]             : AIC=9340.634, Time=5.87 sec\n",
      " ARIMA(0,1,0)(0,1,2)[12]             : AIC=inf, Time=6.03 sec\n",
      " ARIMA(0,1,0)(2,1,0)[12]             : AIC=inf, Time=3.01 sec\n",
      " ARIMA(0,1,0)(2,1,2)[12]             : AIC=inf, Time=7.12 sec\n",
      " ARIMA(1,1,0)(1,1,1)[12]             : AIC=8722.161, Time=5.91 sec\n",
      " ARIMA(1,1,0)(0,1,1)[12]             : AIC=inf, Time=2.91 sec\n",
      " ARIMA(1,1,0)(2,1,1)[12]             : AIC=inf, Time=58.76 sec\n",
      " ARIMA(1,1,0)(1,1,2)[12]             : AIC=8643.689, Time=15.92 sec\n",
      " ARIMA(1,1,0)(0,1,2)[12]             : AIC=inf, Time=9.81 sec\n",
      " ARIMA(1,1,0)(2,1,2)[12]             : AIC=inf, Time=30.67 sec\n",
      " ARIMA(1,1,0)(1,1,3)[12]             : AIC=8652.856, Time=38.59 sec\n",
      " ARIMA(1,1,0)(0,1,3)[12]             : AIC=inf, Time=20.22 sec\n",
      " ARIMA(1,1,0)(2,1,3)[12]             : AIC=inf, Time=nan sec\n",
      " ARIMA(2,1,0)(1,1,2)[12]             : AIC=8515.880, Time=20.60 sec\n",
      " ARIMA(2,1,0)(0,1,2)[12]             : AIC=9137.539, Time=7.65 sec\n",
      " ARIMA(2,1,0)(1,1,1)[12]             : AIC=8654.712, Time=5.83 sec\n",
      " ARIMA(2,1,0)(2,1,2)[12]             : AIC=inf, Time=22.92 sec\n",
      " ARIMA(2,1,0)(1,1,3)[12]             : AIC=8517.913, Time=37.64 sec\n",
      " ARIMA(2,1,0)(0,1,1)[12]             : AIC=inf, Time=2.65 sec\n",
      " ARIMA(2,1,0)(0,1,3)[12]             : AIC=inf, Time=31.07 sec\n",
      " ARIMA(2,1,0)(2,1,1)[12]             : AIC=inf, Time=21.86 sec\n",
      " ARIMA(2,1,0)(2,1,3)[12]             : AIC=inf, Time=42.54 sec\n",
      " ARIMA(3,1,0)(1,1,2)[12]             : AIC=8503.265, Time=17.60 sec\n",
      " ARIMA(3,1,0)(0,1,2)[12]             : AIC=9131.881, Time=10.43 sec\n",
      " ARIMA(3,1,0)(1,1,1)[12]             : AIC=8649.517, Time=6.48 sec\n",
      " ARIMA(3,1,0)(2,1,2)[12]             : AIC=inf, Time=26.55 sec\n",
      " ARIMA(3,1,0)(1,1,3)[12]             : AIC=8505.273, Time=42.21 sec\n",
      " ARIMA(3,1,0)(0,1,1)[12]             : AIC=inf, Time=5.85 sec\n",
      " ARIMA(3,1,0)(0,1,3)[12]             : AIC=inf, Time=32.61 sec\n",
      " ARIMA(3,1,0)(2,1,1)[12]             : AIC=inf, Time=20.65 sec\n",
      " ARIMA(3,1,0)(2,1,3)[12]             : AIC=inf, Time=43.34 sec\n",
      " ARIMA(4,1,0)(1,1,2)[12]             : AIC=8502.075, Time=20.38 sec\n",
      " ARIMA(4,1,0)(0,1,2)[12]             : AIC=9131.222, Time=13.99 sec\n",
      " ARIMA(4,1,0)(1,1,1)[12]             : AIC=8665.864, Time=8.08 sec\n",
      " ARIMA(4,1,0)(2,1,2)[12]             : AIC=inf, Time=35.19 sec\n",
      " ARIMA(4,1,0)(1,1,3)[12]             : AIC=8504.092, Time=46.24 sec\n",
      " ARIMA(4,1,0)(0,1,1)[12]             : AIC=inf, Time=5.32 sec\n",
      " ARIMA(4,1,0)(0,1,3)[12]             : AIC=inf, Time=31.09 sec\n",
      " ARIMA(4,1,0)(2,1,1)[12]             : AIC=inf, Time=29.36 sec\n",
      " ARIMA(4,1,0)(2,1,3)[12]             : AIC=inf, Time=48.88 sec\n",
      " ARIMA(5,1,0)(1,1,2)[12]             : AIC=18.000, Time=10.83 sec\n",
      " ARIMA(5,1,0)(0,1,2)[12]             : AIC=9049.691, Time=14.01 sec\n",
      " ARIMA(5,1,0)(1,1,1)[12]             : AIC=8665.076, Time=8.31 sec\n",
      " ARIMA(5,1,0)(2,1,2)[12]             : AIC=inf, Time=32.15 sec\n",
      " ARIMA(5,1,0)(1,1,3)[12]             : AIC=8503.652, Time=44.55 sec\n",
      " ARIMA(5,1,0)(0,1,1)[12]             : AIC=inf, Time=5.96 sec\n",
      " ARIMA(5,1,0)(0,1,3)[12]             : AIC=inf, Time=45.05 sec\n",
      " ARIMA(5,1,0)(2,1,1)[12]             : AIC=inf, Time=28.34 sec\n",
      " ARIMA(5,1,0)(2,1,3)[12]             : AIC=inf, Time=50.44 sec\n",
      " ARIMA(5,1,1)(1,1,2)[12]             : AIC=8390.784, Time=33.03 sec\n",
      " ARIMA(4,1,1)(1,1,2)[12]             : AIC=inf, Time=31.54 sec\n",
      " ARIMA(5,1,0)(1,1,2)[12] intercept   : AIC=inf, Time=nan sec\n",
      "\n",
      "Best model:  ARIMA(5,1,0)(1,1,2)[12]          \n",
      "Total fit time: 1214.426 seconds\n"
     ]
    }
   ],
   "source": [
    "arima_model = auto_arima(data, start_p = 1, d=1, start_q = 1, \n",
    "                          max_p = 5, max_q = 5, max_d=5, m = 12, \n",
    "                          start_P = 0, D=1, start_Q=0, max_P=5, max_D=5, max_Q=5,\n",
    "                          seasonal = True, \n",
    "                          trace = True, \n",
    "                          error_action ='ignore',   \n",
    "                          suppress_warnings = True,  \n",
    "                          stepwise = True, n_fits=50)          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        SARIMAX Results                                        \n",
      "===============================================================================================\n",
      "Dep. Variable:                                       y   No. Observations:                  741\n",
      "Model:             SARIMAX(5, 1, 0)x(1, 1, [1, 2], 12)   Log Likelihood                   0.000\n",
      "Date:                                 Mon, 10 Jan 2022   AIC                             18.000\n",
      "Time:                                         18:33:23   BIC                             59.313\n",
      "Sample:                                              0   HQIC                            33.941\n",
      "                                                 - 741                                         \n",
      "Covariance Type:                                   opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          2.7908         -0       -inf      0.000       2.791       2.791\n",
      "ar.L2         -1.7292   3.29e-08  -5.26e+07      0.000      -1.729      -1.729\n",
      "ar.L3         -1.8542         -0        inf      0.000      -1.854      -1.854\n",
      "ar.L4          2.7292         -0       -inf      0.000       2.729       2.729\n",
      "ar.L5         -0.9366         -0        inf      0.000      -0.937      -0.937\n",
      "ar.S.L12       0.9962   1.96e-08   5.08e+07      0.000       0.996       0.996\n",
      "ma.S.L12      -1.1482         -0        inf      0.000      -1.148      -1.148\n",
      "ma.S.L24       0.3638   1.55e-08   2.34e+07      0.000       0.364       0.364\n",
      "sigma2      1.953e+04         -0       -inf      0.000    1.95e+04    1.95e+04\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                    nan   Jarque-Bera (JB):               273.00\n",
      "Prob(Q):                               nan   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):                nan   Skew:                             0.00\n",
      "Prob(H) (two-sided):                   nan   Kurtosis:                         0.00\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py:2968: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return self.params / self.bse\n",
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1339: RuntimeWarning: invalid value encountered in true_divide\n",
      "  test_statistic = numer_squared_sum / denom_squared_sum\n",
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:677: RuntimeWarning: invalid value encountered in true_divide\n",
      "  acf = avf[: nlags + 1] / avf[0]\n"
     ]
    }
   ],
   "source": [
    "# To print the summary \n",
    "print(arima_model.summary() ) #Note down the Model and details.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 521\n",
    "test_len = 220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_np = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_np[:train_len]\n",
    "X_test = data_np[train_len:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "521 220\n",
      "(521,) (220,)\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), len(X_test))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit a SARIMAX(5, 1, 0)x(1, 1, [1, 2], 12)  on the training set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SARIMAX(X_train,  \n",
    "                order = (5, 1, 0),\n",
    "                seasonal_order = (1, 1, [1, 2], 12) \n",
    "                )\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "result = model.fit() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                    <td>y</td>                  <th>  No. Observations:  </th>    <td>521</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(5, 1, 0)x(1, 1, [1, 2], 12)</td> <th>  Log Likelihood     </th> <td>-3066.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Mon, 10 Jan 2022</td>           <th>  AIC                </th> <td>6151.329</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>18:35:12</td>               <th>  BIC                </th> <td>6189.404</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                           <td>0</td>                  <th>  HQIC               </th> <td>6166.260</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td> - 521</td>                <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                 <td>opg</td>                 <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    1.2206</td> <td>    0.009</td> <td>  133.005</td> <td> 0.000</td> <td>    1.203</td> <td>    1.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>    <td>   -0.3145</td> <td>    0.027</td> <td>  -11.509</td> <td> 0.000</td> <td>   -0.368</td> <td>   -0.261</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>    <td>   -0.0823</td> <td>    0.068</td> <td>   -1.203</td> <td> 0.229</td> <td>   -0.216</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>    <td>    0.0190</td> <td>    0.101</td> <td>    0.189</td> <td> 0.850</td> <td>   -0.178</td> <td>    0.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>    <td>   -0.0725</td> <td>    0.057</td> <td>   -1.263</td> <td> 0.207</td> <td>   -0.185</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.9969</td> <td>    0.001</td> <td> -770.602</td> <td> 0.000</td> <td>   -0.999</td> <td>   -0.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.0731</td> <td>    0.054</td> <td>   -1.364</td> <td> 0.173</td> <td>   -0.178</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>   -0.5515</td> <td>    0.016</td> <td>  -35.293</td> <td> 0.000</td> <td>   -0.582</td> <td>   -0.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 8458.2175</td> <td>   75.205</td> <td>  112.468</td> <td> 0.000</td> <td> 8310.818</td> <td> 8605.617</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>1.60</td> <th>  Jarque-Bera (JB):  </th> <td>176460.84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.21</td> <th>  Prob(JB):          </th>   <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.11</td> <th>  Skew:              </th>   <td>-4.62</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>93.84</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                        SARIMAX Results                                        \n",
       "===============================================================================================\n",
       "Dep. Variable:                                       y   No. Observations:                  521\n",
       "Model:             SARIMAX(5, 1, 0)x(1, 1, [1, 2], 12)   Log Likelihood               -3066.665\n",
       "Date:                                 Mon, 10 Jan 2022   AIC                           6151.329\n",
       "Time:                                         18:35:12   BIC                           6189.404\n",
       "Sample:                                              0   HQIC                          6166.260\n",
       "                                                 - 521                                         \n",
       "Covariance Type:                                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          1.2206      0.009    133.005      0.000       1.203       1.239\n",
       "ar.L2         -0.3145      0.027    -11.509      0.000      -0.368      -0.261\n",
       "ar.L3         -0.0823      0.068     -1.203      0.229      -0.216       0.052\n",
       "ar.L4          0.0190      0.101      0.189      0.850      -0.178       0.216\n",
       "ar.L5         -0.0725      0.057     -1.263      0.207      -0.185       0.040\n",
       "ar.S.L12      -0.9969      0.001   -770.602      0.000      -0.999      -0.994\n",
       "ma.S.L12      -0.0731      0.054     -1.364      0.173      -0.178       0.032\n",
       "ma.S.L24      -0.5515      0.016    -35.293      0.000      -0.582      -0.521\n",
       "sigma2      8458.2175     75.205    112.468      0.000    8310.818    8605.617\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   1.60   Jarque-Bera (JB):            176460.84\n",
       "Prob(Q):                              0.21   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.11   Skew:                            -4.62\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        93.84\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train prediction\n",
    "start_index = 0\n",
    "end_index = len(X_train)-1\n",
    "train_prediction = result.predict(start_index, end_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction\n",
    "start_index = len(X_train)\n",
    "end_index = len(data)-1\n",
    "prediction = result.predict(start_index, end_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a8f8c53748>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(prediction)\n",
    "plt.plot(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 513.90 RMSE\n",
      "Test Score: 480.68 RMSE\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(X_train, train_prediction))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(X_test, prediction))\n",
    "print('Test Score: %.2f RMSE' % (testScore))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Unrecognized location 'Left corner'. Falling back on 'best'; valid locations are\n",
      "\tbest\n",
      "\tupper right\n",
      "\tupper left\n",
      "\tlower left\n",
      "\tlower right\n",
      "\tright\n",
      "\tcenter left\n",
      "\tcenter right\n",
      "\tlower center\n",
      "\tupper center\n",
      "\tcenter\n",
      "This will raise an exception in 3.3.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = result.predict(start = len(data),  \n",
    "                          end = (len(data)-1) + 300,  \n",
    "                          typ = 'levels') \n",
    "  \n",
    "plt.figure(figsize=(12,8))\n",
    "#plt.plot(X_train, label='Training', color='green')\n",
    "\n",
    "plt.plot(X_test, label='Actual', color='yellow')\n",
    "plt.plot(forecast, label='Forecast', color='cyan')\n",
    "plt.xlabel(\"No. of observations\")\n",
    "plt.ylabel(\"Postion (x-coordinates)\")\n",
    "plt.legend(loc='Left corner')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
